A Framework for Interestingness Measures for Association Rules with Discrete and Continuous Attributes Based on Statistical Validity

Abstract : Assessing rules with interestingness measures is the pillar of successful application of association rules discovery. However, association rules discovered are large in number, some of which are not considered as interesting or significant for the application at hand. In this paper, we present a systematic approach to ascertain the discovered rules, and provide a precise statistical approach supporting this framework. Furthermore, considering that many interestingness measures exist, we propose and compare two established approaches in selecting relevant attributes for the rules prior to rule generation. The proposed strategy combines data mining and statistical measurement techniques, including redundancy analysis, sampling and multivariate statistical analysis, to discard the non-significant rules. In addition to that, we consider real world datasets which are characterized by the uniform and non-uniform data/items distribution with mixture of measurement level throughout the data/items. The proposed unified framework is applied on these datasets to demonstrate its effectiveness in discarding many of the redundant or non-significant rules, while still preserving the high accuracy of the rule set as a whole.
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Communication dans un congrès
Tharam Dillon. 4th IFIP International Conference on Artificial Intelligence in Theory and Practice (AI 2015), Oct 2015, Daejeon, South Korea. IFIP Advances in Information and Communication Technology, AICT-465, pp.119-128, 2015, Artificial Intelligence in Theory and Practice IV. 〈10.1007/978-3-319-25261-2_11〉
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Izwan Shaharanee, Jastini Jamil. A Framework for Interestingness Measures for Association Rules with Discrete and Continuous Attributes Based on Statistical Validity. Tharam Dillon. 4th IFIP International Conference on Artificial Intelligence in Theory and Practice (AI 2015), Oct 2015, Daejeon, South Korea. IFIP Advances in Information and Communication Technology, AICT-465, pp.119-128, 2015, Artificial Intelligence in Theory and Practice IV. 〈10.1007/978-3-319-25261-2_11〉. 〈hal-01383960〉

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